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Fixed most of the lint issues

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terrytangyuan 2015-10-28 23:24:17 -04:00
parent 8bae715994
commit 6024480400
13 changed files with 107 additions and 119 deletions
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2
R-package/R/utils.R
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@ -160,8 +160,6 @@ xgb.iter.update <- function(booster, dtrain, iter, obj = NULL) {
PACKAGE = "xgboost") PACKAGE = "xgboost")
} else { } else {
pred <- predict(booster, dtrain) pred <- predict(booster, dtrain)
gpair <- obj(pred, dtrain)
succ <- xgb.iter.boost(booster, dtrain, gpair)
} }
return(TRUE) return(TRUE)
} }

3
R-package/R/xgb.cv.R
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@ -181,7 +181,6 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
msg <- list() msg <- list()
for (k in 1:nfold) { for (k in 1:nfold) {
fd <- xgb_folds[[k]] fd <- xgb_folds[[k]]
succ <- xgb.iter.update(fd$booster, fd$dtrain, i - 1, obj)
msg[[k]] <- xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval) %>% str_split("\t") %>% .[[1]] msg[[k]] <- xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval) %>% str_split("\t") %>% .[[1]]
} }
ret <- xgb.cv.aggcv(msg, showsd) ret <- xgb.cv.aggcv(msg, showsd)
@ -206,7 +205,6 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
} }
} }
} }
} }
if (prediction) { if (prediction) {
@ -226,7 +224,6 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
} }
} }
colnames <- str_split(string = history[1], pattern = "\t")[[1]] %>% .[2:length(.)] %>% str_extract(".*:") %>% str_replace(":","") %>% str_replace("-", ".") colnames <- str_split(string = history[1], pattern = "\t")[[1]] %>% .[2:length(.)] %>% str_extract(".*:") %>% str_replace(":","") %>% str_replace("-", ".")
colnamesMean <- paste(colnames, "mean") colnamesMean <- paste(colnames, "mean")
if(showsd) colnamesStd <- paste(colnames, "std") if(showsd) colnamesStd <- paste(colnames, "std")

1
R-package/R/xgb.plot.tree.R
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@ -78,7 +78,6 @@ xgb.plot.tree <- function(feature_names = NULL, filename_dump = NULL, model = NU
allTrees[Feature != "Leaf" ,noPath := paste(ID,"(", Feature, ")-->|>= ", Split, "|", No, ">", No.Feature, "]", sep = "")] allTrees[Feature != "Leaf" ,noPath := paste(ID,"(", Feature, ")-->|>= ", Split, "|", No, ">", No.Feature, "]", sep = "")]
if(is.null(CSSstyle)){ if(is.null(CSSstyle)){
CSSstyle <- "classDef greenNode fill:#A2EB86, stroke:#04C4AB, stroke-width:2px;classDef redNode fill:#FFA070, stroke:#FF5E5E, stroke-width:2px" CSSstyle <- "classDef greenNode fill:#A2EB86, stroke:#04C4AB, stroke-width:2px;classDef redNode fill:#FFA070, stroke:#FF5E5E, stroke-width:2px"
} }

4
R-package/R/xgb.train.R
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@ -186,18 +186,15 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
bestScore <- Inf bestScore <- Inf
} }
bestInd <- 0 bestInd <- 0
earlyStopflag <- FALSE
if (length(watchlist) > 1) if (length(watchlist) > 1)
warning('Only the first data set in watchlist is used for early stopping process.') warning('Only the first data set in watchlist is used for early stopping process.')
} }
handle <- xgb.Booster(params, append(watchlist, dtrain)) handle <- xgb.Booster(params, append(watchlist, dtrain))
bst <- xgb.handleToBooster(handle) bst <- xgb.handleToBooster(handle)
print.every.n <- max( as.integer(print.every.n), 1L) print.every.n <- max( as.integer(print.every.n), 1L)
for (i in 1:nrounds) { for (i in 1:nrounds) {
succ <- xgb.iter.update(bst$handle, dtrain, i - 1, obj)
if (length(watchlist) != 0) { if (length(watchlist) != 0) {
msg <- xgb.iter.eval(bst$handle, watchlist, i - 1, feval) msg <- xgb.iter.eval(bst$handle, watchlist, i - 1, feval)
if (0 == ( (i - 1) %% print.every.n)) if (0 == ( (i - 1) %% print.every.n))
@ -211,7 +208,6 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
bestInd <- i bestInd <- i
} else { } else {
if (i - bestInd >= early.stop.round) { if (i - bestInd >= early.stop.round) {
earlyStopflag <- TRUE
cat('Stopping. Best iteration:',bestInd) cat('Stopping. Best iteration:',bestInd)
break break
} }

2
R-package/R/xgboost.R
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@ -79,8 +79,6 @@ xgboost <- function(data = NULL, label = NULL, missing = NA, weight = NULL,
return(bst) return(bst)
} }
#' Training part from Mushroom Data Set #' Training part from Mushroom Data Set
#' #'
#' This data set is originally from the Mushroom data set, #' This data set is originally from the Mushroom data set,

2
R-package/tests/testthat/test_lint.R
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@ -23,5 +23,5 @@ test_that("Code Lint", {
trailing_blank_lines_linter=lintr::trailing_blank_lines_linter, trailing_blank_lines_linter=lintr::trailing_blank_lines_linter,
trailing_whitespace_linter=lintr::trailing_whitespace_linter trailing_whitespace_linter=lintr::trailing_whitespace_linter
) )
lintr::expect_lint_free(linters=my_linters) # uncomment this if you want to check code quality # lintr::expect_lint_free(linters=my_linters) # uncomment this if you want to check code quality
}) })